Modeling the problem. The pump scheduling problem does not have a prior state representation or a single specified reward function. For this reason, state representation learning [1]}, [2]}, [3]} can explore more informative and low dimensional state representations for efficient policy learning. The same applies to reward engineering. Specifying a reward function is difficult once it must encapsulate and balance multiple goals that might be contradictory. A way to overcome this is through inverse RL [4]} approaches to extract a reward function from the demonstrations. Moreover, we presented a suggestion to model the problem as a goal/task-oriented with sparse rewards to allow the evaluation of approaches [5]} that deal with the lack of reward signals.
